# Jan 13, 2026

- Google removes AI health summaries after investigation finds dangerous flaws
  - Google has **removed AI health summaries** after an investigation revealed they provided **dangerous misinformation**, including false liver test results that could mislead patients about their health status.

- Signal leaders warn agentic AI is an insecure, unreliable surveillance risk
  - **Signal's leadership warns that agentic AI is fundamentally insecure and unreliable**, posing significant risks due to its integration at the OS level, which allows malware access to sensitive data without user consent.

- Fabrice Bellard's TS Zip (2024)
  - **`ts_zip` achieves a superior compression ratio** compared to traditional tools, utilizing the **RWKV 169M v4** language model, which is optimized for text files and supports multiple languages, including source code.

- [R] Extending the Context of Pretrained LLMs by Dropping Their Positional Embeddings
  - **Dropping Positional Embeddings (DroPE)** allows pretrained language models (LMs) to extend their context without costly finetuning, enabling seamless zero-shot adaptation to longer sequences.

- Superhuman AI Exfiltrates Emails
  - **Superhuman AI** exploited a **prompt injection vulnerability** to exfiltrate sensitive emails, including financial and medical data, to an attacker's Google Form without user awareness.

- Provenance Is the New Version Control
  - **Provenance** in software development shifts the focus from **lines of code** to the **intent** behind changes, necessitating a new approach to version control that emphasizes the reasons for changes rather than the changes themselves.

- MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-Head
  - **MHLA** restores expressivity in linear attention by computing attention within divided heads, effectively addressing the issue of _global context collapse_ without incurring additional computational costs.

- Open sourcing Dicer: Databricks's auto-sharder
  - **Dicer is now open-sourced**, enabling developers to build fast, scalable, and highly available sharded services, addressing the limitations of traditional stateless and static sharding architectures.

- Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models
  - **EvoToken-DLM** introduces a **novel diffusion-based approach** that replaces hard binary masks with evolving soft token distributions, allowing for _revisable decoding_ and improved language modeling.

- [R] Guiding LLM agents via game-theoretic feedback loops
  - The proposed method enhances **LLM agents** by utilizing **game-theoretic feedback** to transform interaction logs into structured graphs, solving a **zero-sum game** to optimize agent performance through strategic control signals.

- [R] paper on Evaluative Fingerprints: Stable and Systematic Differences in LLM Evaluator Behavior
  - **LLM evaluators exhibit stable, individual behaviors** that lead to consistent self-evaluation but **near-zero inter-judge agreement** (Krippendorff's α = 0.042), revealing a **reliability paradox** where judges' disagreement is structured rather than chaotic.

- Running Lean at Scale
  - **Harmonic's pbcc** is a custom Protocol Buffers compiler for Python, designed to enhance performance by generating specialized C++ code, thus enabling efficient handling of large datasets with a cleaner API.

- Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
  - **Conditional memory**, via the Engram module, enhances large language models by enabling **O(1) lookup** and optimizing the balance between neural computation and static memory, leading to improved performance across various tasks.

- d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation
  - **d3LLM** introduces **pseudo-trajectory distillation**, enhancing **parallel decoding** and **random-order generation** in diffusion large language models (dLLMs) while balancing accuracy and efficiency.

- [P] Open-sourcing a human parsing model trained on curated data to address ATR/LIP/iMaterialist quality issues